Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds

Shin, Hyun Kil

doi:10.1039/d0ra05873d

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…Thus, CNN models with deeper layers, such as ResNet and Inception, were not applied. This was similar to the previous study where increasing the weights within the neural network architecture did not always improve prediction accuracy [20]. Moreover, it is important to search for a model architecture with the minimum number of weights and the highest prediction accuracy, because the use of an excessive number of weights in the model could induce false positives in prediction outcomes [17].…”

Section: Model Prediction Accuracysupporting

confidence: 86%

“…TDEi tensor was calculated based on the electron con guration (EC) of atoms in a molecule. The EC vector was de ned in a previous study by giving a zero, for each unoccupied atomic orbital (AO) and one, for each occupied AO with two different electron spins marked by positive and negative signs [20]. The EC vector can be varied by combining degenerated AOs or electron spins because these electrons possess identical levels of energy.…”

Section: De Nition Of An Electron Con Guration Vectormentioning

confidence: 99%

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Topological Distance-Based Electron Interaction Tensor: A Novel Molecular Structure Representation to Bridge Convolutional Neural Network Studies in Computer Vision to Drug-Like Compound Datasets

Shin¹

2021

Preprint

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Owing to the success achieved by deep learning, researchers are exploringthe application of deep learning in drug discovery to improve the accuracy of prediction models. Significant performance improvement has been achieved by diverse convolutional neural network (CNN) models in computer vision, and the preparation of an input format suitable for CNN is one of the major questions required to be answered in order to harness the advancements in using CNNs for chemical data. It was reported that the models achieved improvement in prediction accuracy, in deep learning studies on molecular structure data; however, the improvement was insufficient from an industry perspective. Furthermore, a recent study suggested that conventional machine learning models can outperform deep learning models on chemical data. As only a limited number of feature calculation methods are available for molecules in deep learning studies, it is crucial to develop more methods to calculate features appropriate for deep learning model development.A topological distance-based electron interaction (TDEi) tensor has been introduced in this study to transform a molecular structure into image-like 3D arrays based on electron interactions (Eis) within a molecule. The prediction accuracy of the CNN model with the TDEi tensor was tested with four datasets: MP (275,131), Lipop (4,193), Esol (1,127), and Freesolv (639), and the models achieved desirable prediction accuracy. Ei is the fundamental level of information that determines the chemical properties of a molecule. Feature space variation was visualized by taking outputs from the middle of the CNN architecture as the CNN model exhibited outstanding performance in automatic feature extraction.The correlation between features from the CNN, and target endpoints was strengthened as outputs were extracted from the deeper layer of the CNN.

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Section: Model Prediction Accuracysupporting

confidence: 86%

Section: De Nition Of An Electron Con Guration Vectormentioning

confidence: 99%

Topological Distance-Based Electron Interaction Tensor: A Novel Molecular Structure Representation to Bridge Convolutional Neural Network Studies in Computer Vision to Drug-Like Compound Datasets

Shin¹

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The EC vector was defined in a previous study by giving a zero for each unoccupied AO and one for each occupied AO with two different electron spins marked by positive and negative signs. 20 The EC vector can be varied by combining degenerated AOs or electron spins because these electrons possess identical levels of energy. It is particularly significant in the case of handling small sized data set to integrate the information for efficient model training.…”

Section: Methodsmentioning

confidence: 99%

“…The TDEi tensor was calculated based on the EC of atoms in a molecule. The EC vector was defined in a previous study by giving a zero for each unoccupied AO and one for each occupied AO with two different electron spins marked by positive and negative signs . The EC vector can be varied by combining degenerated AOs or electron spins because these electrons possess identical levels of energy.…”

Section: Methodsmentioning

confidence: 99%

“…This was similar to the previous study where increasing the weights within the neural network architecture did not always improve prediction accuracy. 20 Moreover, it is important to find a model architecture with the minimum number of weights achieving the highest prediction accuracy because the use of an excessive number of weights in the model could induce false positives in prediction outcomes. 11 Among the four datasets used in this study, the experimental error in MP data was available in the reference.…”

Section: = − ∑ −mentioning

confidence: 99%

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Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds

Shin

2021

ACS Omega

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Deep learning (DL) models in quantitative structure–activity relationship fed the molecular structure directly to the network without using human-designed descriptors by representing molecule as a graph or string (e.g., SMILES code). However, these two representations were oversimplification of real molecules to reflect chemical properties of molecular structures. Given that the choice of molecular representation determines the architecture of the DL model to apply, a novel way of molecular representation can open a way to apply diverse DL networks developed and used in other fields. A topological distance-based electron interaction (TDEi) tensor has been developed in this study inspired by the quantum mechanical model of the molecule, which defines a molecule with electrons and protons. In the TDEi tensor, the atomic orbital (AO) of each atom is represented by an electron configuration (EC) vector, which is a bit string based on the presence and absence of electrons in each AO according to spin indicated by positive and negative signs. Interactions between EC vectors were calculated based on the topological distance between atoms in a molecule. As a molecular structure was translated into 3D array, CNN models (modified VGGNet) were applied using a TDEi tensor to predict four physicochemical properties of drug-like compound datasets: MP (275,131), Lipop (4193), Esol (1127), and Freesolv (639). Models achieved good prediction accuracy. PCA showed that a stronger correlation was observed between the extracted features and the target endpoint as features were extracted from the deeper layer.

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Development of blood brain barrier permeation prediction models for organic and inorganic biocidal active substances

Shin

Lee

et al. 2021

Chemosphere

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Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds

Abstract: Registration, evaluation, and authorization of chemicals (REACH), the regulation of chemicals in use, imposes the characterization and report of the physicochemical properties of compounds.

Cited by 5 publications

References 25 publications

Topological Distance-Based Electron Interaction Tensor: A Novel Molecular Structure Representation to Bridge Convolutional Neural Network Studies in Computer Vision to Drug-Like Compound Datasets

Topological Distance-Based Electron Interaction Tensor: A Novel Molecular Structure Representation to Bridge Convolutional Neural Network Studies in Computer Vision to Drug-Like Compound Datasets

Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds

Development of blood brain barrier permeation prediction models for organic and inorganic biocidal active substances

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